Top Data & Analytics Jobs in New York City, NY

As a Senior Analyst in FP&A at Shutterstock, you'll perform financial modeling, support revenue and expense forecasting, prepare reports, analyze business metrics, and provide insights for strategic decision-making. You'll be involved in tracking KPIs and conducting scenario planning for various initiatives.

As a Staff Data Engineer, Ads at Reddit, you'll lead the analytics engineering within the Ads Data Science team to enhance data quality and automation. Responsibilities include developing robust data pipelines, creating user-friendly tools for data analysis, mentoring other data analysts, and fostering a data-driven culture across the company.

The Operational Risk Analyst will assist in identifying, assessing, and mitigating operational risks within the organization. Responsibilities include coordinating risk activities, conducting risk assessments, preparing governance reports, and maintaining relationships with stakeholders.

The Transfer Analyst plays a crucial role in facilitating IPv4 transfers, providing excellent customer service, managing transactions, and ensuring compliance with transfers. Responsibilities include tracking transfers, answering customer inquiries, and representing the company at conferences.

The role involves performing quantitative research using Python and presenting findings to hedge funds and investment firms. Strong problem-solving skills and an interest in discussing ideas are essential.

As a Senior Data Scientist at Rocket Money, you'll build machine learning-driven features in collaboration with engineering, product, and design teams. You'll also optimize product feedback loops, develop automation for budgeting tools, construct customer segmentation models, run experiments to gauge impact, and ensure accuracy in transaction categorization systems. You'll lead projects, drive strategy, and focus on enhancing customers' financial experiences.

As a Staff LLM Data Scientist, you will design and fine-tune LLMs, build advanced Retrieval Augmented Generation pipelines, implement evaluation frameworks, and deploy models into production. A strong focus on generative AI applications in investment is needed, alongside collaboration with product and engineering teams.